George E.M. Long;Diego Perez-Liebana;Spyridon Samothrakis
{"title":"STEP: A Framework for Automated Point Cost Estimation","authors":"George E.M. Long;Diego Perez-Liebana;Spyridon Samothrakis","doi":"10.1109/TG.2024.3450992","DOIUrl":null,"url":null,"abstract":"In miniature wargames, such as \n<italic>Warhammer 40k</i>\n, players control asymmetrical armies, which include multiple units of different types and strengths. These games often use point costs to balance the armies. Each unit is assigned a point cost, and players have a budget they can spend on units. Calculating accurate point costs can be a tedious manual process, with iterative playtests required. If these point costs do not represent a units true power, the game can get unbalanced as overpowered units can have low point costs. In our previous paper, we proposed an automated way of estimating the point costs using a linear regression approach. We used a turn-based asymmetrical wargame called \n<italic>Wizard Wars</i>\n to test our methods. Players were simulated using Monte Carlo tree search, using different heuristics to represent playstyles. We presented six variants of our method, and show that one method was able to reduce the unbalanced nature of the game by almost half. For this article, we introduce a framework called simple testing and evaluation of points, which allows for further and more granular analysis of point cost estimating methods, by providing a fast, simple, and configurable framework to test methods with. Finally, we compare how our methods do in \n<italic>Wizard Wars</i>\n against expertly chosen point costs.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 4","pages":"927-936"},"PeriodicalIF":1.7000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Games","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10654666/","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In miniature wargames, such as
Warhammer 40k
, players control asymmetrical armies, which include multiple units of different types and strengths. These games often use point costs to balance the armies. Each unit is assigned a point cost, and players have a budget they can spend on units. Calculating accurate point costs can be a tedious manual process, with iterative playtests required. If these point costs do not represent a units true power, the game can get unbalanced as overpowered units can have low point costs. In our previous paper, we proposed an automated way of estimating the point costs using a linear regression approach. We used a turn-based asymmetrical wargame called
Wizard Wars
to test our methods. Players were simulated using Monte Carlo tree search, using different heuristics to represent playstyles. We presented six variants of our method, and show that one method was able to reduce the unbalanced nature of the game by almost half. For this article, we introduce a framework called simple testing and evaluation of points, which allows for further and more granular analysis of point cost estimating methods, by providing a fast, simple, and configurable framework to test methods with. Finally, we compare how our methods do in
Wizard Wars
against expertly chosen point costs.